40 citations · 167 across the 10 of their papers we have counts for
10 papers
IFQA: Interpretable Face Quality Assessment
Byungho Jo, Donghyeon Cho, In Kyu Park +1
Existing face restoration models have relied on general assessment metrics that do not consider the characteristics of facial regions. Recent works have therefore assessed their me…
Weakly-Supervised Stitching Network for Real-World Panoramic Image Generation
Dae-Young Song, Geonsoo Lee, HeeKyung Lee +2
Recently, there has been growing attention on an end-to-end deep learning-based stitching model. However, the most challenging point in deep learning-based stitching is to obtain p…
Domain Adaptation without Source Data
Youngeun Kim, Donghyeon Cho, Kyeongtak Han +2
Domain adaptation assumes that samples from source and target domains are freely accessible during a training phase. However, such an assumption is rarely plausible in the real-wor…
Self-Supervised Fast Adaptation for Denoising via Meta-Learning
Seunghwan Lee, Donghyeon Cho, Jiwon Kim +1
Under certain statistical assumptions of noise, recent self-supervised approaches for denoising have been introduced to learn network parameters without true clean images, and thes…
Key Instance Selection for Unsupervised Video Object Segmentation
Donghyeon Cho, Sungeun Hong, Sungil Kang +1
This paper proposes key instance selection based on video saliency covering objectness and dynamics for unsupervised video object segmentation (UVOS). Our method takes frames seque…
Self-Supervised Video Representation Learning with Space-Time Cubic Puzzles
Dahun Kim, Donghyeon Cho, In So Kweon
Self-supervised tasks such as colorization, inpainting and zigsaw puzzle have been utilized for visual representation learning for still images, when the number of labeled images i…
LinkNet: Relational Embedding for Scene Graph
Sanghyun Woo, Dahun Kim, Donghyeon Cho +1
Objects and their relationships are critical contents for image understanding. A scene graph provides a structured description that captures these properties of an image. However,…
Learning Image Representations by Completing Damaged Jigsaw Puzzles
Dahun Kim, Donghyeon Cho, Donggeun Yoo +1
In this paper, we explore methods of complicating self-supervised tasks for representation learning. That is, we do severe damage to data and encourage a network to recover them. F…
Two-Phase Learning for Weakly Supervised Object Localization
Dahun Kim, Donghyeon Cho, Donggeun Yoo +1
Weakly supervised semantic segmentation and localiza- tion have a problem of focusing only on the most important parts of an image since they use only image-level annota- tions. In…
Weakly- and Self-Supervised Learning for Content-Aware Deep Image Retargeting
Donghyeon Cho, Jinsun Park, Tae-Hyun Oh +2
This paper proposes a weakly- and self-supervised deep convolutional neural network (WSSDCNN) for content-aware image retargeting. Our network takes a source image and a target asp…